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- Paul R. Cohen, Niall M. Adams, Brent Heeringa
- Intell. Data Anal.
- 2007

We describe a statistical signature of chunks and an algorithm for finding chunks. While there is no formal definition of chunks, they may be reliably identified as configurations with low internal entropy or unpredictability and high entropy at their boundaries. We show that the log frequency of a chunk is a measure of its internal entropy. The… (More)

- Micah Adler, Brent Heeringa
- Algorithmica
- 2008

We give a (ln n+1)-approximation for the decision tree (DT) problem. An instance of DT is a set of m binary tests T=(T 1,…,T m ) and a set of n items X=(X 1,…,X n ). The goal is to output a binary tree where each internal node is a test, each leaf is an item and the total external path length of the tree is minimized. Total external path length is the sum… (More)

We propose a novel batch active learning method that leverages the availability of high-quality and efficient sequential active-learning policies by approximating their behavior when applied for k steps. Specifically, our algorithm uses MonteCarlo simulation to estimate the distribution of unlabeled examples selected by a sequential policy over k steps. The… (More)

Let us call a sequence of numbers heapable if they can be sequentially inserted to form a binary tree with the heap property, where each insertion subsequent to the first occurs at a leaf of the tree, i.e. below a previously placed number. In this paper we consider a variety of problems related to heapable sequences and subsequences that do not appear to… (More)

- Michael Gerbush, Brent Heeringa
- CIAA
- 2010

We consider the problem of finding minimum reset sequences in synchronizing automata. The well-known Černý conjecture states that every n-state synchronizing automaton has a reset sequence with length at most (n − 1). While this conjecture gives an upper bound on the length of every reset sequence, it does not directly address the problem of finding the… (More)

- Paul R. Cohen, Brent Heeringa, Niall M. Adams
- Pattern Detection and Discovery
- 2002

This paper describes an unsupervised algorithm for segmenting categorical time series into episodes. The Voting-Experts algorithm first collects statistics about the frequency and boundary entropy of ngrams, then passes a window over the series and has two “expert methods” decide where in the window boundaries should be drawn. The algorithm successfully… (More)

- Glencora Borradaile, Brent Heeringa, Gordon T. Wilfong
- J. Discrete Algorithms
- 2012

We study a constrained version of the knapsack problem in which dependencies between items are given by the adjacencies of a graph. In the 1-neighbour knapsack problem, an item can be selected only if at least one of its neighbours is also selected. In the all-neighbours knapsack problem, an item can be selected only if all its neighbours are also selected.… (More)

- Paul R. Cohen, Brent Heeringa, Niall M. Adams
- ICDM
- 2002

This paper describes an unsupervised olgorirhm f o r segmenting categorical time series inro episodes. The VOTING-EXPERTS algorithm first collects starisrics about the frequency and boundav entmpy of ngrams. then passes a window over rhe series and has two “expert methods ” decide where in rhe window boundaries should be drawn. The algorirhm successfully… (More)

- Marc S. Atkin, Gary W. King, David L. Westbrook, Brent Heeringa, Paul R. Cohen
- Agents
- 2001

The Hierarchical Agent Control Architecture (HAC) is a general toolkit for specifying an agent's behavior. HAC supports action abstraction, resource management, sensor integration, and is well suited to controlling large numbers of agents in dynamic environments. It relies on three hierarchies: action, sensor, and context. The action hierarchy controls the… (More)

- Brent Heeringa, Paul R. Cohen
- Winter Simulation Conference
- 2000

Defeat mechanisms are strategies for achieving victory over an opponent. Although defeat mechanisms often rely on influencing the opponent psychologically and emotionally, most simulations of warfare do not model these "soft" factors, they model only victory by attrition. To create more accurate, adaptable, and believable systems, we must be able to model a… (More)